灰色预测法在情报研究中的应用研究综述

[目的/意义] 灰色预测法可有效处理情报研究中广泛存在的小样本数据，通过对灰色预测法在情报研究中的应用情况进行梳理，总结其在应用过程中存在的不足，为灰色预测法在情报研究中的进一步应用提供参考。[方法/过程] 通过综述情报研究中涉及灰色预测法的相关文献，从数据选取、模型构建和解决的问题等方面对情报研究中灰色预测法的应用进行概述，总结当前情报研究中灰色预测法的应用所存在的问题，并提出改进建议。[结果/结论] 在方法应用上，已有研究主要采用数列灰预测，且模型集中在单变量灰色预测模型，根据预测对象不同，灰色预测法已经在包括期刊分析、图书馆运行管理、热点主题分析及科研机构评价方面得到了很好的应用，未来可根据预测对象特点及研究目标尝试不同的灰色预测方法，扩宽灰色预测法在其他方面的情报研究问题中的应用。     

Quality flaw prediction in Spanish Wikipedia: A case of study with verifiability flaws

In this work, we present the first quality flaw prediction study for articles containing the two most frequent verifiability flaws in Spanish Wikipedia: articles which do not cite any references or sources at all (denominated Unreferenced) and articles that need additional citations for verification (so-called Refimprove). Based on the underlying characteristics of each flaw, different state-of-the-art approaches were evaluated. For articles not citing any references, a well-established rule-based approach was evaluated and interesting findings show that some of them suffer from Refimprove flaw instead. Likewise, for articles that need additional citations for verification, the well-known PU learning and one-class classification approaches were evaluated. Besides, new methods were compared and a new feature was also proposed to model this latter flaw. The results showed that new methods such as under-bagged decision trees with sum or majority voting rules, biased-SVM, and centroid-based balanced SVM, perform best in comparison with the ones previously published.     

基于二模复杂网络的隐性知识发现方法研究——以潜在药物靶点挖掘为例

[目的/意义] 通过构建二模复杂网络模型，揭示隐藏在海量文献中的隐性知识。[方法/过程] 通过NetworkX复杂网络工具包，依据任意两个节点的共现关系构建二模复杂网络模型；对网络模型中节点的共现关系进行加权，计算网络的拓扑信息并进行AP聚类，提取节点间的直接关系；采用AUC方法对AA、JC、加权改进的wAA和wJC等4种链路预测算法进行评价，遴选出最合适的预测算法，并对复杂网络的隐性关系进行预测分析。[结果/结论] 以潜在药物靶点挖掘为例进行的实证研究结果表明，wAA链路预测算法为最优的链路预测算法；二模复杂网络模型、指标和方法体系在美国化学文摘社数据库中的药物靶点挖掘中具有一定的有效性。下一步计划在其他数据库中或其他研究领域中进行尝试，以进一步验证该模型的通用性和有效性。     

Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods

Cross-Company Churn Prediction (CCCP) is a domain of research where one company (target) is lacking enough data and can use data from another company (source) to predict customer churn successfully. To support CCCP, the cross-company data is usually transformed to a set of similar normal distribution of target company data prior to building a CCCP model. However, it is still unclear which data transformation method is most effective in CCCP. Also, the impact of data transformation methods on CCCP model performance using different classifiers have not been comprehensively explored in the telecommunication sector. In this study, we devised a model for CCCP using data transformation methods (i.e., log, z-score, rank and box-cox) and presented not only an extensive comparison to validate the impact of these transformation methods in CCCP, but also evaluated the performance of underlying baseline classifiers (i.e., Naive Bayes (NB), K-Nearest Neighbour (KNN), Gradient Boosted Tree (GBT), Single Rule Induction (SRI) and Deep learner Neural net (DP)) for customer churn prediction in telecommunication sector using the above mentioned data transformation methods. We performed experiments on publicly available datasets related to the telecommunication sector. The results demonstrated that most of the data transformation methods (e.g., log, rank, and box-cox) improve the performance of CCCP significantly. However, the Z-Score data transformation method could not achieve better results as compared to the rest of the data transformation methods in this study. Moreover, it is also investigated that the CCCP model based on NB outperform on transformed data and DP, KNN and GBT performed on the average, while SRI classifier did not show significant results in term of the commonly used evaluation measures (i.e., probability of detection, probability of false alarm, area under the curve and g-mean).     

一种基于图卷积自编码模型的多维度学科知识网络融合方法

[目的/意义] 针对包含单一类型知识单元的知识网络难以全面反映学科知识结构的问题，提出一种从多维度进行知识网络结构融合的方法，为学科领域知识结构挖掘提供借鉴。[方法/过程] 利用LDA及TF-IDF方法抽取学科知识单元，然后运用语义相似度和关键词共现分析方法构建3个学科知识子网络：主题网络、关键词网络和实体网络，并采用空间节点传递对齐方法对齐子网络节点，接着设计基于图卷积操作的自编码模型对知识节点进行表示，最后通过计算余弦相似度重构学科知识网络。[结果/结论] 实验部分以人工智能领域为例，构建融合主题、关键词和实体的学科知识网络并展开分析，实验结果表明，本文所提方法能有效地揭示学科领域研究内容和知识结构，为学科知识发现与组织研究提供有益参考。     

数学构造性方法的应用

从数学方法的角度 ,通过若干实例的分析 ,阐明了“数学构造性方法”的具体应用。     

体育运动中神经-肌肉疲劳研究的新进展

简要回顾神经-肌肉疲劳的研究历史,并从生理学角度综述近年来国内外有关中枢性疲劳与外周疲劳的理论研究成果以及在实验研究方面取得的新进展.有关中枢性疲劳,着重介绍5-羟色胺、多巴胺、乙酰胆碱等中枢神经递质参与运动性疲劳的形成与恢复的新证据与新理论(可能的机制).有关外周疲劳机制,从神经肌肉接头处的超微结构到骨骼肌细胞的亚细胞结构等可能发生疲劳的部位,将近年来的研究成果作简要概括.这些研究成果表明有关运动性疲劳的生理学研究进入到亚细胞水平与分子水平.     

河南省城镇居民体育消费结构分析

采用问卷调查法，对河南省城镇居民体育消费行为进行调查。结果显示，河南省居民体育消费能力低于全国水平，实物性体育消费占主导地位，观赏性体育消费相对滞后，参与性体育消费有快速增长势头。提出发展河南省城镇居民体育消费的对策。     

影响我国体育产业发展的变动因素分析

体育产业是现代经济发展的新形式，是发达国家的经济之柱产业。本文从我国产业结构、我国城镇与农村收入和消费结构变动、我国加入WTO及我国将举办2008年北京奥运会等变动因素，对我国体育产业发展的影响因素进行了分析研究。     

对我国部分项目优秀教练员能力结构的研究

采用文献资料调研、专家及问卷调查和数理统计等方法对我国部分项目优秀教练员的能力结构进行研究,探讨从事不同运动项目的优秀教练员所具有的能力结构与共同特征.